{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5525bf60",
   "metadata": {},
   "source": [
    "# Xception\n",
    "Xception 是由 François Chollet 在 2016 年提出的一种深度卷积神经网络结构，它是对 Inception 系列模型的思想延伸和改进。Xception 全称 Extreme Inception ，核心思想是将 Inception 模块的设计理念推向极致。\n",
    "\n",
    "传统的Inception模块可以简化为下图所示的结构,现进行1x1的卷积,然后进行分组卷积.\n",
    "\n",
    "  ![alt text](resources/xception_inception.png \"Title\")\n",
    "\n",
    "xception将其推至极致,将分组卷积变为Depthwise卷积,注意1x1卷积和Depthwise的卷积顺序并不重要,但是两者之间没有非线性激活函数,它可能会导致信息丢失从而降低性能.\n",
    "\n",
    "![alt text](resources/xception_xception.png \"Title\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5d1e83",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "70544e18",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use device:  cuda\n"
     ]
    }
   ],
   "source": [
    "# 自动重新加载外部module，使得修改代码之后无需重新import\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from hdd.device.utils import get_device\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchvision import datasets, transforms\n",
    "\n",
    "# 设置训练数据的路径\n",
    "DATA_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置TensorBoard的路径\n",
    "TENSORBOARD_ROOT = \"~/workspace/hands-dirty-on-dl/dataset\"\n",
    "# 设置预训练模型参数路径\n",
    "TORCH_HUB_PATH = \"~/workspace/hands-dirty-on-dl/pretrained_models\"\n",
    "torch.hub.set_dir(TORCH_HUB_PATH)\n",
    "# 挑选最合适的训练设备\n",
    "DEVICE = get_device([\"cuda\", \"cpu\"])\n",
    "print(\"Use device: \", DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d1e1748",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hdd.dataset.imagenette_in_memory import ImagenetteInMemory\n",
    "from hdd.data_util.auto_augmentation import ImageNetPolicy\n",
    "\n",
    "from hdd.data_util.transforms import RandomResize\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "TRAIN_MEAN = [0.4625, 0.4580, 0.4295]\n",
    "TRAIN_STD = [0.2452, 0.2390, 0.2469]\n",
    "train_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        RandomResize([256, 296, 384]),  # 随机在三个size中选择一个进行resize\n",
    "        transforms.RandomCrop(224),\n",
    "        transforms.RandomHorizontalFlip(),\n",
    "        ImageNetPolicy(),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "val_dataset_transforms = transforms.Compose(\n",
    "    [\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize(mean=TRAIN_MEAN, std=TRAIN_STD),\n",
    "    ]\n",
    ")\n",
    "train_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"train\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=train_dataset_transforms,\n",
    ")\n",
    "val_dataset = ImagenetteInMemory(\n",
    "    root=DATA_ROOT,\n",
    "    split=\"val\",\n",
    "    size=\"full\",\n",
    "    download=True,\n",
    "    transform=val_dataset_transforms,\n",
    ")\n",
    "\n",
    "\n",
    "def build_dataloader(batch_size, train_dataset, val_dataset):\n",
    "    train_dataloader = DataLoader(\n",
    "        train_dataset, batch_size=batch_size, shuffle=True, num_workers=8\n",
    "    )\n",
    "    val_dataloader = DataLoader(\n",
    "        val_dataset, batch_size=batch_size, shuffle=False, num_workers=8\n",
    "    )\n",
    "    return train_dataloader, val_dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7e8af81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#Parameter: 20877754\n",
      "Epoch: 1/200 Train Loss: 2.1695 Accuracy: 0.2399 Time: 20.39241  | Val Loss: 1.8684 Accuracy: 0.3918\n",
      "Epoch: 2/200 Train Loss: 1.9309 Accuracy: 0.3627 Time: 20.11423  | Val Loss: 1.9931 Accuracy: 0.4387\n",
      "Epoch: 3/200 Train Loss: 1.7668 Accuracy: 0.4458 Time: 20.26295  | Val Loss: 1.6324 Accuracy: 0.5162\n",
      "Epoch: 4/200 Train Loss: 1.6283 Accuracy: 0.5136 Time: 19.85652  | Val Loss: 1.6535 Accuracy: 0.5289\n",
      "Epoch: 5/200 Train Loss: 1.5206 Accuracy: 0.5664 Time: 20.04877  | Val Loss: 1.2323 Accuracy: 0.7034\n",
      "Epoch: 6/200 Train Loss: 1.4481 Accuracy: 0.6000 Time: 19.98762  | Val Loss: 1.3113 Accuracy: 0.6645\n",
      "Epoch: 7/200 Train Loss: 1.3681 Accuracy: 0.6348 Time: 19.82165  | Val Loss: 1.2171 Accuracy: 0.7011\n",
      "Epoch: 8/200 Train Loss: 1.3253 Accuracy: 0.6531 Time: 19.99217  | Val Loss: 1.3202 Accuracy: 0.6828\n",
      "Epoch: 9/200 Train Loss: 1.2872 Accuracy: 0.6735 Time: 20.50219  | Val Loss: 1.1059 Accuracy: 0.7541\n",
      "Epoch: 10/200 Train Loss: 1.2222 Accuracy: 0.7012 Time: 20.14038  | Val Loss: 1.0182 Accuracy: 0.7972\n",
      "Epoch: 11/200 Train Loss: 1.1869 Accuracy: 0.7159 Time: 20.07308  | Val Loss: 0.9970 Accuracy: 0.8036\n",
      "Epoch: 12/200 Train Loss: 1.1577 Accuracy: 0.7303 Time: 20.05111  | Val Loss: 0.9726 Accuracy: 0.8115\n",
      "Epoch: 13/200 Train Loss: 1.1338 Accuracy: 0.7405 Time: 20.04947  | Val Loss: 1.0854 Accuracy: 0.7682\n",
      "Epoch: 14/200 Train Loss: 1.1137 Accuracy: 0.7463 Time: 20.03276  | Val Loss: 0.9829 Accuracy: 0.8082\n",
      "Epoch: 15/200 Train Loss: 1.0830 Accuracy: 0.7646 Time: 20.02435  | Val Loss: 0.9485 Accuracy: 0.8321\n",
      "Epoch: 16/200 Train Loss: 1.0682 Accuracy: 0.7728 Time: 20.10549  | Val Loss: 0.9734 Accuracy: 0.8145\n",
      "Epoch: 17/200 Train Loss: 1.0573 Accuracy: 0.7702 Time: 19.95674  | Val Loss: 0.9690 Accuracy: 0.8099\n",
      "Epoch: 18/200 Train Loss: 1.0307 Accuracy: 0.7828 Time: 19.97127  | Val Loss: 0.8927 Accuracy: 0.8459\n",
      "Epoch: 19/200 Train Loss: 0.9985 Accuracy: 0.8025 Time: 20.01419  | Val Loss: 0.9544 Accuracy: 0.8214\n",
      "Epoch: 20/200 Train Loss: 1.0059 Accuracy: 0.7961 Time: 19.85333  | Val Loss: 0.8824 Accuracy: 0.8553\n",
      "Epoch: 21/200 Train Loss: 0.9793 Accuracy: 0.8060 Time: 19.87137  | Val Loss: 0.9370 Accuracy: 0.8242\n",
      "Epoch: 22/200 Train Loss: 0.9788 Accuracy: 0.8085 Time: 20.17092  | Val Loss: 0.8342 Accuracy: 0.8757\n",
      "Epoch: 23/200 Train Loss: 0.9632 Accuracy: 0.8136 Time: 20.06888  | Val Loss: 0.9809 Accuracy: 0.7975\n",
      "Epoch: 24/200 Train Loss: 0.9525 Accuracy: 0.8196 Time: 20.30567  | Val Loss: 0.8069 Accuracy: 0.8790\n",
      "Epoch: 25/200 Train Loss: 0.9442 Accuracy: 0.8212 Time: 19.95279  | Val Loss: 0.8440 Accuracy: 0.8701\n",
      "Epoch: 26/200 Train Loss: 0.9363 Accuracy: 0.8272 Time: 20.01256  | Val Loss: 0.8257 Accuracy: 0.8810\n",
      "Epoch: 27/200 Train Loss: 0.9127 Accuracy: 0.8332 Time: 19.72938  | Val Loss: 0.8464 Accuracy: 0.8609\n",
      "Epoch: 28/200 Train Loss: 0.9162 Accuracy: 0.8340 Time: 20.36756  | Val Loss: 0.8742 Accuracy: 0.8515\n",
      "Epoch: 29/200 Train Loss: 0.8927 Accuracy: 0.8431 Time: 19.87743  | Val Loss: 0.8321 Accuracy: 0.8736\n",
      "Epoch: 30/200 Train Loss: 0.8900 Accuracy: 0.8437 Time: 19.84581  | Val Loss: 0.8354 Accuracy: 0.8713\n",
      "Epoch: 31/200 Train Loss: 0.8895 Accuracy: 0.8475 Time: 19.87172  | Val Loss: 0.8759 Accuracy: 0.8535\n",
      "Epoch: 32/200 Train Loss: 0.8811 Accuracy: 0.8515 Time: 19.88340  | Val Loss: 0.7825 Accuracy: 0.8902\n",
      "Epoch: 33/200 Train Loss: 0.8690 Accuracy: 0.8574 Time: 20.19758  | Val Loss: 0.8149 Accuracy: 0.8762\n",
      "Epoch: 34/200 Train Loss: 0.8550 Accuracy: 0.8604 Time: 19.83406  | Val Loss: 0.8383 Accuracy: 0.8622\n",
      "Epoch: 35/200 Train Loss: 0.8600 Accuracy: 0.8577 Time: 19.75810  | Val Loss: 0.8251 Accuracy: 0.8721\n",
      "Epoch: 36/200 Train Loss: 0.8561 Accuracy: 0.8604 Time: 19.98105  | Val Loss: 0.7572 Accuracy: 0.9060\n",
      "Epoch: 37/200 Train Loss: 0.8435 Accuracy: 0.8647 Time: 19.94947  | Val Loss: 0.7702 Accuracy: 0.8930\n",
      "Epoch: 38/200 Train Loss: 0.8377 Accuracy: 0.8670 Time: 19.98492  | Val Loss: 0.7962 Accuracy: 0.8887\n",
      "Epoch: 39/200 Train Loss: 0.8344 Accuracy: 0.8703 Time: 20.24983  | Val Loss: 0.7564 Accuracy: 0.9027\n",
      "Epoch: 40/200 Train Loss: 0.8346 Accuracy: 0.8703 Time: 20.10037  | Val Loss: 0.7991 Accuracy: 0.8866\n",
      "Epoch: 41/200 Train Loss: 0.8320 Accuracy: 0.8702 Time: 20.14311  | Val Loss: 0.7490 Accuracy: 0.9065\n",
      "Epoch: 42/200 Train Loss: 0.8216 Accuracy: 0.8743 Time: 20.31665  | Val Loss: 0.7788 Accuracy: 0.8848\n",
      "Epoch: 43/200 Train Loss: 0.8312 Accuracy: 0.8705 Time: 20.56599  | Val Loss: 0.7603 Accuracy: 0.8966\n",
      "Epoch: 44/200 Train Loss: 0.8041 Accuracy: 0.8781 Time: 20.36664  | Val Loss: 0.7424 Accuracy: 0.9004\n",
      "Epoch: 45/200 Train Loss: 0.8031 Accuracy: 0.8799 Time: 20.32711  | Val Loss: 0.7624 Accuracy: 0.8943\n",
      "Epoch: 46/200 Train Loss: 0.7988 Accuracy: 0.8819 Time: 20.49893  | Val Loss: 0.7437 Accuracy: 0.9050\n",
      "Epoch: 47/200 Train Loss: 0.7954 Accuracy: 0.8832 Time: 20.66471  | Val Loss: 0.7531 Accuracy: 0.9006\n",
      "Epoch: 48/200 Train Loss: 0.7958 Accuracy: 0.8838 Time: 20.65058  | Val Loss: 0.7676 Accuracy: 0.9032\n",
      "Epoch: 49/200 Train Loss: 0.7796 Accuracy: 0.8930 Time: 20.58094  | Val Loss: 0.7658 Accuracy: 0.9009\n",
      "Epoch: 50/200 Train Loss: 0.7850 Accuracy: 0.8875 Time: 20.61854  | Val Loss: 0.7843 Accuracy: 0.8950\n",
      "Epoch: 51/200 Train Loss: 0.7792 Accuracy: 0.8935 Time: 20.65034  | Val Loss: 0.7908 Accuracy: 0.8925\n",
      "Epoch: 52/200 Train Loss: 0.7841 Accuracy: 0.8903 Time: 20.61701  | Val Loss: 0.7549 Accuracy: 0.9009\n",
      "Epoch: 53/200 Train Loss: 0.7706 Accuracy: 0.8975 Time: 20.35743  | Val Loss: 0.7245 Accuracy: 0.9068\n",
      "Epoch: 54/200 Train Loss: 0.7678 Accuracy: 0.8933 Time: 20.38837  | Val Loss: 0.7204 Accuracy: 0.9190\n",
      "Epoch: 55/200 Train Loss: 0.7605 Accuracy: 0.8990 Time: 20.44540  | Val Loss: 0.7304 Accuracy: 0.9098\n",
      "Epoch: 56/200 Train Loss: 0.7606 Accuracy: 0.9005 Time: 20.04713  | Val Loss: 0.7213 Accuracy: 0.9121\n",
      "Epoch: 57/200 Train Loss: 0.7419 Accuracy: 0.9085 Time: 20.10448  | Val Loss: 0.7559 Accuracy: 0.8986\n",
      "Epoch: 58/200 Train Loss: 0.7413 Accuracy: 0.9063 Time: 20.08169  | Val Loss: 0.7413 Accuracy: 0.9060\n",
      "Epoch: 59/200 Train Loss: 0.7523 Accuracy: 0.9038 Time: 20.06920  | Val Loss: 0.7171 Accuracy: 0.9180\n",
      "Epoch: 60/200 Train Loss: 0.7409 Accuracy: 0.9103 Time: 20.15077  | Val Loss: 0.7358 Accuracy: 0.9078\n",
      "Epoch: 61/200 Train Loss: 0.7338 Accuracy: 0.9118 Time: 21.00168  | Val Loss: 0.7235 Accuracy: 0.9106\n",
      "Epoch: 62/200 Train Loss: 0.7345 Accuracy: 0.9134 Time: 20.90299  | Val Loss: 0.7242 Accuracy: 0.9139\n",
      "Epoch: 63/200 Train Loss: 0.7385 Accuracy: 0.9082 Time: 20.80980  | Val Loss: 0.7523 Accuracy: 0.9014\n",
      "Epoch: 64/200 Train Loss: 0.7217 Accuracy: 0.9152 Time: 20.75105  | Val Loss: 0.7246 Accuracy: 0.9139\n",
      "Epoch: 65/200 Train Loss: 0.7319 Accuracy: 0.9133 Time: 20.51319  | Val Loss: 0.7209 Accuracy: 0.9113\n",
      "Epoch: 66/200 Train Loss: 0.7279 Accuracy: 0.9121 Time: 20.67401  | Val Loss: 0.6981 Accuracy: 0.9231\n",
      "Epoch: 67/200 Train Loss: 0.7103 Accuracy: 0.9202 Time: 20.02471  | Val Loss: 0.7036 Accuracy: 0.9220\n",
      "Epoch: 68/200 Train Loss: 0.7204 Accuracy: 0.9198 Time: 20.03034  | Val Loss: 0.7472 Accuracy: 0.9047\n",
      "Epoch: 69/200 Train Loss: 0.7266 Accuracy: 0.9154 Time: 20.09013  | Val Loss: 0.7118 Accuracy: 0.9172\n",
      "Epoch: 70/200 Train Loss: 0.7107 Accuracy: 0.9206 Time: 20.15588  | Val Loss: 0.7293 Accuracy: 0.9126\n",
      "Epoch: 71/200 Train Loss: 0.7033 Accuracy: 0.9248 Time: 20.12447  | Val Loss: 0.6970 Accuracy: 0.9203\n",
      "Epoch: 72/200 Train Loss: 0.6996 Accuracy: 0.9250 Time: 20.55364  | Val Loss: 0.7031 Accuracy: 0.9182\n",
      "Epoch: 73/200 Train Loss: 0.7091 Accuracy: 0.9212 Time: 20.64798  | Val Loss: 0.6996 Accuracy: 0.9231\n",
      "Epoch: 74/200 Train Loss: 0.6971 Accuracy: 0.9288 Time: 20.32262  | Val Loss: 0.7156 Accuracy: 0.9200\n",
      "Epoch: 75/200 Train Loss: 0.6881 Accuracy: 0.9312 Time: 20.48802  | Val Loss: 0.6906 Accuracy: 0.9246\n",
      "Epoch: 76/200 Train Loss: 0.6886 Accuracy: 0.9293 Time: 20.33203  | Val Loss: 0.6992 Accuracy: 0.9218\n",
      "Epoch: 77/200 Train Loss: 0.6993 Accuracy: 0.9265 Time: 20.37976  | Val Loss: 0.6995 Accuracy: 0.9210\n",
      "Epoch: 78/200 Train Loss: 0.6875 Accuracy: 0.9319 Time: 20.52454  | Val Loss: 0.6864 Accuracy: 0.9276\n",
      "Epoch: 79/200 Train Loss: 0.6814 Accuracy: 0.9327 Time: 20.44635  | Val Loss: 0.6858 Accuracy: 0.9259\n",
      "Epoch: 80/200 Train Loss: 0.6863 Accuracy: 0.9303 Time: 20.22806  | Val Loss: 0.6912 Accuracy: 0.9256\n",
      "Epoch: 81/200 Train Loss: 0.6863 Accuracy: 0.9307 Time: 20.64610  | Val Loss: 0.6888 Accuracy: 0.9259\n",
      "Epoch: 82/200 Train Loss: 0.6724 Accuracy: 0.9379 Time: 20.66099  | Val Loss: 0.6908 Accuracy: 0.9284\n",
      "Epoch: 83/200 Train Loss: 0.6850 Accuracy: 0.9310 Time: 20.76827  | Val Loss: 0.6855 Accuracy: 0.9269\n",
      "Epoch: 84/200 Train Loss: 0.6788 Accuracy: 0.9328 Time: 20.79133  | Val Loss: 0.6816 Accuracy: 0.9338\n",
      "Epoch: 85/200 Train Loss: 0.6674 Accuracy: 0.9400 Time: 20.25415  | Val Loss: 0.6905 Accuracy: 0.9279\n",
      "Epoch: 86/200 Train Loss: 0.6689 Accuracy: 0.9397 Time: 20.56710  | Val Loss: 0.6791 Accuracy: 0.9282\n",
      "Epoch: 87/200 Train Loss: 0.6704 Accuracy: 0.9374 Time: 20.12863  | Val Loss: 0.6937 Accuracy: 0.9241\n",
      "Epoch: 88/200 Train Loss: 0.6723 Accuracy: 0.9356 Time: 20.10581  | Val Loss: 0.6917 Accuracy: 0.9259\n",
      "Epoch: 89/200 Train Loss: 0.6633 Accuracy: 0.9409 Time: 20.33297  | Val Loss: 0.6744 Accuracy: 0.9343\n",
      "Epoch: 90/200 Train Loss: 0.6682 Accuracy: 0.9372 Time: 20.44186  | Val Loss: 0.6855 Accuracy: 0.9243\n",
      "Epoch: 91/200 Train Loss: 0.6559 Accuracy: 0.9440 Time: 20.49948  | Val Loss: 0.6690 Accuracy: 0.9348\n",
      "Epoch: 92/200 Train Loss: 0.6471 Accuracy: 0.9475 Time: 20.34385  | Val Loss: 0.6717 Accuracy: 0.9343\n",
      "Epoch: 93/200 Train Loss: 0.6782 Accuracy: 0.9351 Time: 20.57238  | Val Loss: 0.7394 Accuracy: 0.9098\n",
      "Epoch: 94/200 Train Loss: 0.6699 Accuracy: 0.9373 Time: 20.35154  | Val Loss: 0.6949 Accuracy: 0.9243\n",
      "Epoch: 95/200 Train Loss: 0.6553 Accuracy: 0.9441 Time: 20.49488  | Val Loss: 0.6723 Accuracy: 0.9322\n",
      "Epoch: 96/200 Train Loss: 0.6541 Accuracy: 0.9429 Time: 20.56713  | Val Loss: 0.6822 Accuracy: 0.9259\n",
      "Epoch: 97/200 Train Loss: 0.6563 Accuracy: 0.9430 Time: 20.38611  | Val Loss: 0.6826 Accuracy: 0.9297\n",
      "Epoch: 98/200 Train Loss: 0.6574 Accuracy: 0.9440 Time: 20.13875  | Val Loss: 0.6769 Accuracy: 0.9317\n",
      "Epoch: 99/200 Train Loss: 0.6408 Accuracy: 0.9519 Time: 20.08278  | Val Loss: 0.6938 Accuracy: 0.9264\n",
      "Epoch: 100/200 Train Loss: 0.6415 Accuracy: 0.9518 Time: 20.08381  | Val Loss: 0.6925 Accuracy: 0.9282\n",
      "Epoch: 101/200 Train Loss: 0.6452 Accuracy: 0.9474 Time: 20.12911  | Val Loss: 0.6794 Accuracy: 0.9297\n",
      "Epoch: 102/200 Train Loss: 0.6392 Accuracy: 0.9513 Time: 20.16632  | Val Loss: 0.6745 Accuracy: 0.9353\n",
      "Epoch: 103/200 Train Loss: 0.6383 Accuracy: 0.9495 Time: 20.06863  | Val Loss: 0.6783 Accuracy: 0.9307\n",
      "Epoch: 104/200 Train Loss: 0.6267 Accuracy: 0.9568 Time: 20.05309  | Val Loss: 0.6716 Accuracy: 0.9348\n",
      "Epoch: 105/200 Train Loss: 0.6288 Accuracy: 0.9537 Time: 19.97532  | Val Loss: 0.6637 Accuracy: 0.9322\n",
      "Epoch: 106/200 Train Loss: 0.6303 Accuracy: 0.9534 Time: 19.99559  | Val Loss: 0.6710 Accuracy: 0.9361\n",
      "Epoch: 107/200 Train Loss: 0.6282 Accuracy: 0.9541 Time: 20.06871  | Val Loss: 0.6786 Accuracy: 0.9307\n",
      "Epoch: 108/200 Train Loss: 0.6257 Accuracy: 0.9567 Time: 20.10694  | Val Loss: 0.6726 Accuracy: 0.9340\n",
      "Epoch: 109/200 Train Loss: 0.6305 Accuracy: 0.9534 Time: 20.09827  | Val Loss: 0.6805 Accuracy: 0.9315\n",
      "Epoch: 110/200 Train Loss: 0.6260 Accuracy: 0.9544 Time: 20.20874  | Val Loss: 0.6709 Accuracy: 0.9312\n",
      "Epoch: 111/200 Train Loss: 0.6303 Accuracy: 0.9530 Time: 20.18333  | Val Loss: 0.6743 Accuracy: 0.9330\n",
      "Epoch: 112/200 Train Loss: 0.6250 Accuracy: 0.9571 Time: 20.03383  | Val Loss: 0.6891 Accuracy: 0.9256\n",
      "Epoch: 113/200 Train Loss: 0.6248 Accuracy: 0.9551 Time: 20.58549  | Val Loss: 0.7063 Accuracy: 0.9205\n",
      "Epoch: 114/200 Train Loss: 0.6249 Accuracy: 0.9551 Time: 20.52218  | Val Loss: 0.6706 Accuracy: 0.9299\n",
      "Epoch: 115/200 Train Loss: 0.6220 Accuracy: 0.9556 Time: 20.71801  | Val Loss: 0.6704 Accuracy: 0.9348\n",
      "Epoch: 116/200 Train Loss: 0.6257 Accuracy: 0.9555 Time: 20.68340  | Val Loss: 0.6634 Accuracy: 0.9361\n",
      "Epoch: 117/200 Train Loss: 0.6170 Accuracy: 0.9597 Time: 20.52291  | Val Loss: 0.6709 Accuracy: 0.9330\n",
      "Epoch: 118/200 Train Loss: 0.6190 Accuracy: 0.9591 Time: 20.71963  | Val Loss: 0.6686 Accuracy: 0.9348\n",
      "Epoch: 119/200 Train Loss: 0.6179 Accuracy: 0.9571 Time: 20.69549  | Val Loss: 0.6848 Accuracy: 0.9274\n",
      "Epoch: 120/200 Train Loss: 0.6123 Accuracy: 0.9606 Time: 20.63366  | Val Loss: 0.6680 Accuracy: 0.9338\n",
      "Epoch: 121/200 Train Loss: 0.6107 Accuracy: 0.9611 Time: 20.34758  | Val Loss: 0.6718 Accuracy: 0.9335\n",
      "Epoch: 122/200 Train Loss: 0.6053 Accuracy: 0.9637 Time: 20.30633  | Val Loss: 0.6631 Accuracy: 0.9391\n",
      "Epoch: 123/200 Train Loss: 0.6125 Accuracy: 0.9612 Time: 20.42717  | Val Loss: 0.6846 Accuracy: 0.9276\n",
      "Epoch: 124/200 Train Loss: 0.6067 Accuracy: 0.9638 Time: 20.36530  | Val Loss: 0.6621 Accuracy: 0.9363\n",
      "Epoch: 125/200 Train Loss: 0.6093 Accuracy: 0.9625 Time: 20.68412  | Val Loss: 0.6581 Accuracy: 0.9383\n",
      "Epoch: 126/200 Train Loss: 0.6029 Accuracy: 0.9636 Time: 20.30470  | Val Loss: 0.6631 Accuracy: 0.9358\n",
      "Epoch: 127/200 Train Loss: 0.5993 Accuracy: 0.9662 Time: 20.06224  | Val Loss: 0.6756 Accuracy: 0.9345\n",
      "Epoch: 128/200 Train Loss: 0.5963 Accuracy: 0.9667 Time: 20.27871  | Val Loss: 0.6650 Accuracy: 0.9355\n",
      "Epoch: 129/200 Train Loss: 0.5987 Accuracy: 0.9649 Time: 20.66916  | Val Loss: 0.6709 Accuracy: 0.9378\n",
      "Epoch: 130/200 Train Loss: 0.5983 Accuracy: 0.9677 Time: 20.25928  | Val Loss: 0.6679 Accuracy: 0.9310\n",
      "Epoch: 131/200 Train Loss: 0.6025 Accuracy: 0.9651 Time: 20.06839  | Val Loss: 0.6707 Accuracy: 0.9332\n",
      "Epoch: 132/200 Train Loss: 0.5962 Accuracy: 0.9675 Time: 20.06682  | Val Loss: 0.6520 Accuracy: 0.9409\n",
      "Epoch: 133/200 Train Loss: 0.6022 Accuracy: 0.9654 Time: 20.07937  | Val Loss: 0.6548 Accuracy: 0.9391\n",
      "Epoch: 134/200 Train Loss: 0.5933 Accuracy: 0.9690 Time: 20.08701  | Val Loss: 0.6565 Accuracy: 0.9376\n",
      "Epoch: 135/200 Train Loss: 0.5903 Accuracy: 0.9696 Time: 20.08771  | Val Loss: 0.6598 Accuracy: 0.9330\n",
      "Epoch: 136/200 Train Loss: 0.5919 Accuracy: 0.9695 Time: 20.09596  | Val Loss: 0.6586 Accuracy: 0.9373\n",
      "Epoch: 137/200 Train Loss: 0.5900 Accuracy: 0.9694 Time: 20.11019  | Val Loss: 0.6601 Accuracy: 0.9371\n",
      "Epoch: 138/200 Train Loss: 0.5945 Accuracy: 0.9678 Time: 20.08964  | Val Loss: 0.6624 Accuracy: 0.9350\n",
      "Epoch: 139/200 Train Loss: 0.5841 Accuracy: 0.9718 Time: 20.04061  | Val Loss: 0.6588 Accuracy: 0.9394\n",
      "Epoch: 140/200 Train Loss: 0.5863 Accuracy: 0.9699 Time: 20.01253  | Val Loss: 0.6601 Accuracy: 0.9355\n",
      "Epoch: 141/200 Train Loss: 0.5905 Accuracy: 0.9688 Time: 20.01751  | Val Loss: 0.6642 Accuracy: 0.9366\n",
      "Epoch: 142/200 Train Loss: 0.5855 Accuracy: 0.9710 Time: 20.03563  | Val Loss: 0.6646 Accuracy: 0.9368\n",
      "Epoch: 143/200 Train Loss: 0.5838 Accuracy: 0.9705 Time: 20.20083  | Val Loss: 0.6551 Accuracy: 0.9404\n",
      "Epoch: 144/200 Train Loss: 0.5814 Accuracy: 0.9733 Time: 20.07075  | Val Loss: 0.6578 Accuracy: 0.9368\n",
      "Epoch: 145/200 Train Loss: 0.5839 Accuracy: 0.9703 Time: 20.15041  | Val Loss: 0.6563 Accuracy: 0.9378\n",
      "Epoch: 146/200 Train Loss: 0.5834 Accuracy: 0.9718 Time: 20.14458  | Val Loss: 0.6640 Accuracy: 0.9358\n",
      "Epoch: 147/200 Train Loss: 0.5784 Accuracy: 0.9734 Time: 20.03966  | Val Loss: 0.6614 Accuracy: 0.9350\n",
      "Epoch: 148/200 Train Loss: 0.5779 Accuracy: 0.9745 Time: 20.09734  | Val Loss: 0.6624 Accuracy: 0.9358\n",
      "Epoch: 149/200 Train Loss: 0.5814 Accuracy: 0.9717 Time: 20.10074  | Val Loss: 0.6536 Accuracy: 0.9414\n",
      "Epoch: 150/200 Train Loss: 0.5846 Accuracy: 0.9710 Time: 20.05364  | Val Loss: 0.6475 Accuracy: 0.9401\n",
      "Epoch: 151/200 Train Loss: 0.5803 Accuracy: 0.9721 Time: 20.09524  | Val Loss: 0.6522 Accuracy: 0.9373\n",
      "Epoch: 152/200 Train Loss: 0.5742 Accuracy: 0.9744 Time: 20.13274  | Val Loss: 0.6554 Accuracy: 0.9361\n",
      "Epoch: 153/200 Train Loss: 0.5743 Accuracy: 0.9747 Time: 20.04349  | Val Loss: 0.6496 Accuracy: 0.9394\n",
      "Epoch: 154/200 Train Loss: 0.5709 Accuracy: 0.9764 Time: 20.05350  | Val Loss: 0.6510 Accuracy: 0.9386\n",
      "Epoch: 155/200 Train Loss: 0.5748 Accuracy: 0.9748 Time: 20.06334  | Val Loss: 0.6543 Accuracy: 0.9358\n",
      "Epoch: 156/200 Train Loss: 0.5764 Accuracy: 0.9733 Time: 20.04534  | Val Loss: 0.6550 Accuracy: 0.9386\n",
      "Epoch: 157/200 Train Loss: 0.5782 Accuracy: 0.9733 Time: 20.11437  | Val Loss: 0.6542 Accuracy: 0.9376\n",
      "Epoch: 158/200 Train Loss: 0.5723 Accuracy: 0.9768 Time: 20.07768  | Val Loss: 0.6511 Accuracy: 0.9429\n",
      "Epoch: 159/200 Train Loss: 0.5789 Accuracy: 0.9744 Time: 20.07929  | Val Loss: 0.6577 Accuracy: 0.9373\n",
      "Epoch: 160/200 Train Loss: 0.5741 Accuracy: 0.9754 Time: 20.06833  | Val Loss: 0.6495 Accuracy: 0.9406\n",
      "Epoch: 161/200 Train Loss: 0.5750 Accuracy: 0.9732 Time: 20.03797  | Val Loss: 0.6632 Accuracy: 0.9368\n",
      "Epoch: 162/200 Train Loss: 0.5711 Accuracy: 0.9763 Time: 20.10836  | Val Loss: 0.6471 Accuracy: 0.9417\n",
      "Epoch: 163/200 Train Loss: 0.5678 Accuracy: 0.9782 Time: 20.04666  | Val Loss: 0.6448 Accuracy: 0.9424\n",
      "Epoch: 164/200 Train Loss: 0.5686 Accuracy: 0.9770 Time: 20.12542  | Val Loss: 0.6490 Accuracy: 0.9399\n",
      "Epoch: 165/200 Train Loss: 0.5690 Accuracy: 0.9776 Time: 20.05163  | Val Loss: 0.6498 Accuracy: 0.9406\n",
      "Epoch: 166/200 Train Loss: 0.5701 Accuracy: 0.9777 Time: 20.05769  | Val Loss: 0.6442 Accuracy: 0.9417\n",
      "Epoch: 167/200 Train Loss: 0.5675 Accuracy: 0.9797 Time: 20.06400  | Val Loss: 0.6497 Accuracy: 0.9383\n",
      "Epoch: 168/200 Train Loss: 0.5738 Accuracy: 0.9752 Time: 20.04388  | Val Loss: 0.6476 Accuracy: 0.9389\n",
      "Epoch: 169/200 Train Loss: 0.5672 Accuracy: 0.9794 Time: 20.05944  | Val Loss: 0.6452 Accuracy: 0.9422\n",
      "Epoch: 170/200 Train Loss: 0.5700 Accuracy: 0.9771 Time: 20.12512  | Val Loss: 0.6494 Accuracy: 0.9383\n",
      "Epoch: 171/200 Train Loss: 0.5633 Accuracy: 0.9793 Time: 20.07456  | Val Loss: 0.6457 Accuracy: 0.9394\n",
      "Epoch: 172/200 Train Loss: 0.5638 Accuracy: 0.9804 Time: 20.10566  | Val Loss: 0.6491 Accuracy: 0.9386\n",
      "Epoch: 173/200 Train Loss: 0.5679 Accuracy: 0.9792 Time: 20.13066  | Val Loss: 0.6479 Accuracy: 0.9417\n",
      "Epoch: 174/200 Train Loss: 0.5650 Accuracy: 0.9797 Time: 20.08786  | Val Loss: 0.6531 Accuracy: 0.9396\n",
      "Epoch: 175/200 Train Loss: 0.5649 Accuracy: 0.9795 Time: 20.08535  | Val Loss: 0.6496 Accuracy: 0.9404\n",
      "Epoch: 176/200 Train Loss: 0.5679 Accuracy: 0.9779 Time: 20.04465  | Val Loss: 0.6499 Accuracy: 0.9386\n",
      "Epoch: 177/200 Train Loss: 0.5710 Accuracy: 0.9750 Time: 20.09334  | Val Loss: 0.6429 Accuracy: 0.9409\n",
      "Epoch: 178/200 Train Loss: 0.5661 Accuracy: 0.9776 Time: 20.04038  | Val Loss: 0.6428 Accuracy: 0.9414\n",
      "Epoch: 179/200 Train Loss: 0.5585 Accuracy: 0.9825 Time: 20.07209  | Val Loss: 0.6460 Accuracy: 0.9417\n",
      "Epoch: 180/200 Train Loss: 0.5627 Accuracy: 0.9809 Time: 20.04164  | Val Loss: 0.6437 Accuracy: 0.9396\n",
      "Epoch: 181/200 Train Loss: 0.5607 Accuracy: 0.9814 Time: 20.09875  | Val Loss: 0.6426 Accuracy: 0.9424\n",
      "Epoch: 182/200 Train Loss: 0.5593 Accuracy: 0.9806 Time: 20.09821  | Val Loss: 0.6431 Accuracy: 0.9427\n",
      "Epoch: 183/200 Train Loss: 0.5625 Accuracy: 0.9797 Time: 20.08481  | Val Loss: 0.6430 Accuracy: 0.9419\n",
      "Epoch: 184/200 Train Loss: 0.5604 Accuracy: 0.9805 Time: 20.11761  | Val Loss: 0.6433 Accuracy: 0.9406\n",
      "Epoch: 185/200 Train Loss: 0.5614 Accuracy: 0.9803 Time: 20.07615  | Val Loss: 0.6419 Accuracy: 0.9419\n",
      "Epoch: 186/200 Train Loss: 0.5615 Accuracy: 0.9807 Time: 20.10808  | Val Loss: 0.6404 Accuracy: 0.9419\n",
      "Epoch: 187/200 Train Loss: 0.5617 Accuracy: 0.9794 Time: 20.06945  | Val Loss: 0.6459 Accuracy: 0.9417\n",
      "Epoch: 188/200 Train Loss: 0.5632 Accuracy: 0.9789 Time: 20.09087  | Val Loss: 0.6507 Accuracy: 0.9396\n",
      "Epoch: 189/200 Train Loss: 0.5612 Accuracy: 0.9805 Time: 20.11499  | Val Loss: 0.6456 Accuracy: 0.9401\n",
      "Epoch: 190/200 Train Loss: 0.5624 Accuracy: 0.9797 Time: 20.07034  | Val Loss: 0.6446 Accuracy: 0.9411\n",
      "Epoch: 191/200 Train Loss: 0.5655 Accuracy: 0.9782 Time: 20.09898  | Val Loss: 0.6438 Accuracy: 0.9417\n",
      "Epoch: 192/200 Train Loss: 0.5651 Accuracy: 0.9796 Time: 20.09056  | Val Loss: 0.6462 Accuracy: 0.9373\n",
      "Epoch: 193/200 Train Loss: 0.5572 Accuracy: 0.9819 Time: 20.08398  | Val Loss: 0.6442 Accuracy: 0.9399\n",
      "Epoch: 194/200 Train Loss: 0.5593 Accuracy: 0.9801 Time: 20.08552  | Val Loss: 0.6440 Accuracy: 0.9417\n",
      "Epoch: 195/200 Train Loss: 0.5604 Accuracy: 0.9795 Time: 20.12056  | Val Loss: 0.6476 Accuracy: 0.9399\n",
      "Epoch: 196/200 Train Loss: 0.5626 Accuracy: 0.9797 Time: 20.08132  | Val Loss: 0.6461 Accuracy: 0.9414\n",
      "Epoch: 197/200 Train Loss: 0.5606 Accuracy: 0.9798 Time: 20.07712  | Val Loss: 0.6417 Accuracy: 0.9422\n",
      "Epoch: 198/200 Train Loss: 0.5610 Accuracy: 0.9814 Time: 20.05585  | Val Loss: 0.6405 Accuracy: 0.9424\n",
      "Epoch: 199/200 Train Loss: 0.5608 Accuracy: 0.9810 Time: 20.06586  | Val Loss: 0.6439 Accuracy: 0.9396\n",
      "Epoch: 200/200 Train Loss: 0.5634 Accuracy: 0.9797 Time: 20.06439  | Val Loss: 0.6424 Accuracy: 0.9404\n",
      "#Parameter: 20877754 Accuracy: 0.9403821656050956\n"
     ]
    }
   ],
   "source": [
    "from hdd.models.cnn.xception import XceptionNet\n",
    "from hdd.train.classification_utils import (\n",
    "    naive_train_classification_model,\n",
    "    eval_image_classifier,\n",
    ")\n",
    "from hdd.models.nn_utils import count_trainable_parameter\n",
    "\n",
    "\n",
    "def train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    lr=1e-3,\n",
    "    weight_decay=1e-3,\n",
    "    max_epochs=200,\n",
    ") -> tuple[XceptionNet, dict[str, list[float]]]:\n",
    "    net = XceptionNet(num_classes=10, dropout=0.5).to(DEVICE)\n",
    "    print(f\"#Parameter: {count_trainable_parameter(net)}\")\n",
    "    criteria = nn.CrossEntropyLoss(label_smoothing=0.1)\n",
    "    optimizer = torch.optim.AdamW(net.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(\n",
    "        optimizer, max_epochs, eta_min=lr / 100\n",
    "    )\n",
    "    training_stats = naive_train_classification_model(\n",
    "        net,\n",
    "        criteria,\n",
    "        max_epochs,\n",
    "        train_dataloader,\n",
    "        val_dataloader,\n",
    "        DEVICE,\n",
    "        optimizer,\n",
    "        scheduler,\n",
    "        verbose=True,\n",
    "    )\n",
    "    return net, training_stats\n",
    "\n",
    "\n",
    "train_dataloader, val_dataloader = build_dataloader(64, train_dataset, val_dataset)\n",
    "\n",
    "net, width_multiplier_1 = train_net(\n",
    "    train_dataloader,\n",
    "    val_dataloader,\n",
    "    lr=0.001,\n",
    "    weight_decay=0,\n",
    ")\n",
    "\n",
    "eval_result = eval_image_classifier(net, val_dataloader.dataset, DEVICE)\n",
    "ss = [result.gt_label == result.predicted_label for result in eval_result]\n",
    "print(f\"#Parameter: {count_trainable_parameter(net)} Accuracy: {sum(ss) / len(ss)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c6c2d68e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,12))\n",
    "fields = width_multiplier_1.keys()\n",
    "for i, field in enumerate(fields):\n",
    "    plt.subplot(2, 2, i+1)\n",
    "    plt.plot(width_multiplier_1[field], label=\"Width-1\", linestyle=\"--\")\n",
    "    plt.legend()\n",
    "    plt.title(field)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18c2f2e9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pytorch-cu124",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
